AI 3D Piping Route Layout for Complex Plant Design Constraints
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Solution Overview
Problem
The design and implementation of complex piping systems in industrial plants, such as power generation systems, are cumbersome and often result in non-optimized routes, increasing design time and operational inefficiencies.
Innovation Solution
A three-dimensional (3-D) piping layout is generated using artificial intelligence-driven training and modeling, involving a scale increasing model and vector routing model to optimize pipe routes based on various criteria, which can be iteratively updated with feedback for improved efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to design piping routes, then users can control and adjust routes, but the design process becomes cumbersome and time-consuming
Solution Approach 1:
The patent replaces manual mechanical piping design processes with an AI-based automated system. The AI model generates optimized piping routes automatically based on input parameters such as start/end points, constraints, and optimization criteria, eliminating the need for manual route plotting and reducing design time significantly while maintaining control through programmable parameters.
Solution Approach 2:
The piping design system performs self-service by automatically generating optimized routes without requiring extensive manual intervention. The AI model independently evaluates multiple routing options, applies constraints, and produces final pipe layouts based on the provided criteria, allowing the system to serve itself rather than requiring continuous user guidance for each design decision.
2Manufacturing precision
If repetitive redesign is performed to optimize piping routes, then route optimization is achieved, but design time and implementation cost increase
Solution Approach 1:
The AI model performs preliminary action by generating multiple optimized piping route options in advance based on predetermined criteria and constraints. Instead of requiring iterative manual redesign, the system pre-calculates optimized routes considering factors such as minimal length, optimal positioning, and constraint compliance, providing ready-to-implement solutions that reduce or eliminate repetitive redesign cycles.
Solution Approach 2:
The system achieves route optimization by dynamically changing and evaluating multiple parameter combinations (route parameters) through the AI model. It varies parameters such as pipe orientation, positioning, and routing paths according to optimization criteria, automatically selecting the best configuration without requiring manual iterative adjustments, thus achieving precision optimization without time loss.
3Loss of time
If AI-driven automated route generation is used, then design time is reduced, but the system complexity increases
Solution Approach 1:
The AI model serves as an intermediary between the user's design requirements and the final piping layout generation. It mediates the complexity by accepting high-level parameters (start points, constraints, optimization goals) from the user and automatically translating them into detailed optimized routes, shielding the user from underlying system complexity while delivering simplified, optimized results.
4Ease of operation
If manual piping design is performed, then user control is maintained, but the piping routes become non-optimized
Solution Approach 1:
The system provides dynamic control through programmable parameters and adjustable optimization criteria. Users can dynamically configure the AI model's behavior by setting parameters such as constraint priorities, optimization weights, and route preferences, allowing the system to adapt to different design scenarios while maintaining user control over the optimization process and final outcomes.
Data Source
AI summary
A system includes a processing circuitry and a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry cause the processing circuitry to perform operation including receiving one or more route criteria and identifying a number of pipes, wherein the number of pipes is based on the one or more route criteria. The piping also includes generating a vector route, storing the vector route, generating a limiting zone, and performing an iterative process including determining one or more vector routes for each of the identified number of pipes. Further, the piping system also includes optimizing a route solution based on the one or more vector routes based on an optimization parameter and outputting a three-dimensional pipe layout, wherein the three-dimensional pipe layout is transmitted to an external platform for display via a user interface.